Complete the function calc$\_$genrated$\_$stats to compute various network properties such as network diameter, characteristic path length $($CPL$),$ average clustering coefficient, transitivity, and assortativity for each grph in a provided list. If any grph is not fully connected, consider using its largest connected component for analysis. The results are returned as a dict where each property is described by a string key, with a list of corresponding values for each grph$.$

Then, implement the function compare$\_$genrated$\_$to$\_$grnd$\_$truth to perform a one$-$sample t$-$test on the empirical network properties against the sampled values from each grph model. It compares the diameter, CPL$,$ average clustering coefficient, transitivity, and assortativity. The results are returned as a dict of p$-$values.

Lastly, develop the function plt$\_$grph$\_$stats to visually represent the distribution of each network property $($e$.$g$.,$ CPL$,$ average clustering$)$ through boxplts. The boxplts are arranged in a single plt with five side$-$by$-$side subplts, each depicting one property. The overall plt is labeled with the generator name, and each individual boxplt is labeled with the corresponding property on the y$-$axis.

def calc$\_$genrated$\_$stats$($grphs: List$[$nx$.$Grph$])->$ Dict$[$str$,$ list$]$:

$"""$

Inputs:

grphs: a list of NetworkX grph object

Returns:

a dict of grph stats

$"""$

return grph$\_$stats

def compare$\_$genrated$\_$to$\_$grnd$\_$truth$($grnd$\_$truth$\_$features: Dict$[$str$,$ float$],$ genrated$\_$features: Dict$[$str$,$ List$[$float$]])->$ Dict$[$str$,$ float$]$:

$"""$

Inputs:

grnd$\_$truth$\_$features: a dict of grph stats

genrated$\_$features: a dict of grph stats

Returns:

a dict of one$-$sample t$-$test

$"""$

return p$\_$vals

def plt$\_$grph$\_$stats$($grph$\_$stats: List$[$Dict$[$str$,$ list$]],$ save: bool$=$False$)->$ None:

$"""$

Inputs:

grph$\_$stats: a dict of grph stats

Returns:

None

$"""$